DM4. Support error recovery.
Automated approaches for identifying and classifying salient content within papers are susceptible to errors.
In addition to systematic errors, these methods may also make relevance errors when integrated into user-facing systems like an augmented reading interface.
For example, suggested content that is important to one reader may be deemed irrelevant by another reader under a particular context.
Reflecting guidelines in developing imperfect AI-infused user applications [1]}, we aimed to design Scim to retain the agency of users in efficiently recovering from and dismissing fallible AI assistance.
